On information metrics for spatial coding

Souza, Bryan C.; Pavão, Rodrigo; Belchior, Hindiael; Tort, Adriano Bretanha Lopes

doi:10.1101/189084

Cited by 7 publications

(9 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To estimate the spatial information contained in the firing rate of each cell, we computed the I sec metric as described in (Skaggs et al, 1993; Souza et al, 2018), as it is equivalent to the Mutual Information from the average firing rate (over trials) in the N space bins using the following definition: …”

Section: Resultsmentioning

confidence: 99%

A quick and easy way to estimate entropy and mutual information for neuroscience

Zbili

Rama

2020

Preprint

View full text Add to dashboard Cite

Calculations of entropy of a signal or mutual information between two variables are valuable analytical tools in the field of neuroscience. They can be applied to all types of data, capture nonlinear interactions and are model independent. Yet the limited size and number of recordings one can collect in a series of experiments makes their calculation highly prone to sampling bias. Mathematical methods to overcome this so called “sampling disaster” exist, but require significant expertise, great time and computational costs. As such, there is a need for a simple, unbiased and computationally efficient tool for reliable entropy and mutual information estimation. In this paper, we propose that application of entropy-coding compression algorithms widely used in text and image compression fulfill these requirements. By simply saving the signal in PNG picture format and measuring the size of the file on the hard drive, we can reliably estimate entropy through different conditions. Furthermore, with some simple modifications of the PNG file, we can also estimate mutual information between a stimulus and the observed responses into multiple trials. We show this using White noise signals, electrophysiological signals and histological data. Although this method does not give an absolute value of entropy or mutual information, it is mathematically correct, and its simplicity and broad use make it a powerful tool for their estimation through experiments.

show abstract

Section: Resultsmentioning

confidence: 99%

A quick and easy way to estimate entropy and mutual information for neuroscience

Zbili

Rama

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…3f). Using an information-theoretic framework 48–50 , we computed the mutual information (MI) between calcium activity and spatial location for each cell and expressed the results in bits 47 . To circumvent the inherent issue of sparse sampling in 15min long recordings, we computed the mean MI and confidence interval using 30 bootstrap samples (50% data sampling per sample, Fig.…”

Section: Resultsmentioning

confidence: 99%

Conjunctive spatial and idiothetic codes are topographically organized in the GABAergic cells of the lateral septum

Veldt

Etter

Mosser

et al. 2020

Preprint

View full text Add to dashboard Cite

The relevance of the hippocampal spatial code for downstream neuronal populations, in particular its main subcortical output, the lateral septum (LS), is still poorly understood. Here, we addressed this knowledge gap by first clarifying the organization of LS afferents and efferents via retrograde and anterograde trans-synaptic tracing. We found that mouse LS receives inputs from hippocampal subregions CA1, CA3, and subiculum, and in turn projects directly to the lateral hypothalamus (LH), ventral tegmental area (VTA), and medial septum (MS). Next, we functionally characterized the spatial tuning properties of LS GABAergic cells, the principal cells composing the LS, via calcium imaging combined with unbiased analytical methods. We identified a significant number of cells that are modulated by place (38.01%), speed (23.71%), acceleration (27.84%), and head-direction (23.09%), and conjunctions of these properties, with spatial tuning comparable to hippocampal CA1 and CA3 place cells. Bayesian decoding of position on the basis of LS place cells accurately reflected the location of the animal. The distributions of cells exhibiting these properties formed gradients along the anterior-posterior axis of the LS, directly reflecting the organization of hippocampal inputs to the LS. A portion of LS place cells showed stable fields over the course of multiple days, potentially reflecting long-term episodic memory. Together, our findings demonstrate that the LS accurately and robustly represents spatial and idiothetic information and is uniquely positioned to relay this information from the hippocampus to the VTA, LH, and MS, thus occupying a key position within this distributed spatial memory network.

show abstract

“…Where N is the total number of spatial bins, p i is the occupancy probability of the animal in 735 the i-th spatial bin, p j is the probability to obtain a firing rate amongst one of 4 non 736 overlapping quartiles of firing rates and p i,j is the joint probability of the animal to be in the i-737 th spatial bin with a firing rate in the j-th quartile. The Mutual Information was then 738 normalized with a surrogate based distribution to correct possible bias due to basal firing 739 rate (Souza et al, 2018). 740…”

Section: Spatial Information 726mentioning

confidence: 99%

Dynamic control of hippocampal spatial coding resolution by local visual cues

Bourboulou

Marti

Michon

et al. 2018

Preprint

View full text Add to dashboard Cite

1Animals can flexibly navigate in their environment. This ability is thought to rely on an 2 internal cognitive map. An open question concerns the influence of local sensory cues on the 3 cognitive map and notably their putative contribution to setting its spatial resolution. Here 4 we compared the firing of hippocampal principal cells in mice navigating virtual reality 5 environments in the presence or absence of local visual cues (virtual 3D objects). Objects 6 improved the spatial representation both quantitatively (higher proportion of place cells) 7 and qualitatively (smaller place fields with increased spatial selectivity and stability). This 8 gain in spatial coding resolution was more pronounced near the objects and could be rapidly 9 tuned by their manipulations. In addition, place cells displayed improved theta phase 10 precession in the presence of objects. Thus the hippocampal mapping system can 11 dynamically adjust its spatial coding resolution to local sensory cues available in the 12 environment. 13All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

show abstract

On information metrics for spatial coding

Cited by 7 publications

References 28 publications

A quick and easy way to estimate entropy and mutual information for neuroscience

A quick and easy way to estimate entropy and mutual information for neuroscience

Conjunctive spatial and idiothetic codes are topographically organized in the GABAergic cells of the lateral septum

Dynamic control of hippocampal spatial coding resolution by local visual cues

Contact Info

Product

Resources

About